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import torch as t |
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import numpy as np |
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from jukebox.sample import sample_level |
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from jukebox.utils.torch_utils import assert_shape |
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from jukebox.hparams import Hyperparams |
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def repeat(x, n, dim): |
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if dim == -1: |
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dim = len(x.shape) - 1 |
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return x.reshape(int(np.prod(x.shape[:dim+1])), 1, int(np.prod(x.shape[dim+1:]))).repeat(1,n,1).reshape(*x.shape[:dim], n * x.shape[dim], *x.shape[dim+1:]) |
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class DummyPrior: |
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def __init__(self, n_ctx, level, levels): |
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self.n_ctx = n_ctx |
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self.level = level |
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self.levels = levels |
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self.downsamples = (8,4,4) |
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self.cond_downsample = self.downsamples[level+1] if level != self.levels - 1 else None |
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self.raw_to_tokens = int(np.prod(self.downsamples[:level+1])) |
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self.sample_length = self.n_ctx*self.raw_to_tokens |
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print(f"Level:{level}, Cond downsample:{self.cond_downsample}, Raw to tokens:{self.raw_to_tokens}, Sample length:{self.sample_length}") |
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def get_y(self, labels, start): |
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y = labels['y'].clone() |
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y[:, 2] = self.sample_length |
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y[:, 1:2] = y[:, 1:2] + start * self.raw_to_tokens |
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return y |
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def get_z_conds(self, zs, start, end): |
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if self.level != self.levels - 1: |
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assert start % self.cond_downsample == end % self.cond_downsample == 0 |
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z_cond = zs[self.level + 1][:,start//self.cond_downsample:end//self.cond_downsample] |
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assert z_cond.shape[1] == self.n_ctx//self.cond_downsample |
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z_conds = [z_cond] |
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else: |
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z_conds = None |
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return z_conds |
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def ancestral_sample(self, n_samples, z_conds=None, y=None): |
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z = t.zeros((n_samples, self.n_ctx), dtype=t.long, device='cuda') + \ |
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t.arange(0, self.n_ctx, dtype=t.long, device='cuda').view(1, self.n_ctx) |
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if z_conds is not None: |
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z_cond = z_conds[0] |
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assert_shape(z_cond, (n_samples, self.n_ctx // 4)) |
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assert (z // 4 == repeat(z_cond, 4, 1)).all(), f'z: {z}, z_cond: {z_cond}, diff: {(z // 4) - repeat(z_cond, 4, 1)}' |
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return z |
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def primed_sample(self, n_samples, z, z_conds=None, y=None): |
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prime = z.shape[1] |
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assert_shape(z, (n_samples, prime)) |
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start = z[:,-1:] + 1 |
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z_rest = (t.arange(0, self.n_ctx - prime, dtype=t.long, device='cuda').view(1, self.n_ctx - prime) + start).view(n_samples, self.n_ctx - prime) |
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z = t.cat([z, z_rest], dim=1) |
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if z_conds is not None: |
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z_cond = z_conds[0] |
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assert_shape(z_cond, (n_samples, self.n_ctx // 4)) |
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assert (z // 4 == repeat(z_cond, 4, 1)).all(), f'z: {z}, z_cond: {z_cond}, diff: {(z // 4) - repeat(z_cond, 4, 1)}' |
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return z |
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def _sample(zs, labels, priors, sample_levels, hps): |
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for level in reversed(sample_levels): |
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prior = priors[level] |
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total_length = (hps.sample_length * hps.n_segment)//prior.raw_to_tokens |
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hop_length = hps.hop_lengths[level] |
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zs = sample_level(zs, labels[level], dict(), level, prior, total_length, hop_length, hps) |
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return zs |
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def test_ancestral_sample(labels, priors, hps): |
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sample_levels = list(range(hps.levels)) |
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zs = [t.zeros(hps.n_samples,0,dtype=t.long, device='cuda') for _ in range(hps.levels)] |
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zs = _sample(zs, labels, priors, sample_levels, hps) |
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for z in zs: |
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total_length = z.shape[1] |
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assert ((z - t.arange(0, total_length, dtype=t.long, device='cuda').view(1, total_length)) == 0).all() |
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print("dummy ancestral sample passed") |
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def test_primed_sample(labels, priors, hps): |
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sample_levels = list(range(hps.levels)) |
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start = t.tensor([15, 23, 11, 9], dtype=t.long, device='cuda').view(4, 1) |
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zs_in = [] |
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zs = [] |
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for i in reversed(range(3)): |
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n_ctx = 8192*(4**i) |
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n_prime = n_ctx // 4 |
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z_prime = t.arange(0, n_prime, dtype=t.long, device='cuda').view(1, n_prime) % (2*(4**i)) |
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z_rest = t.randint(-10, -1, size=(1, n_ctx - n_prime), dtype=t.long, device='cuda') |
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z_in = t.cat([z_prime, z_rest], dim=1) + (4**i)*start |
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zs_in.append(z_in) |
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zs.append(z_prime + (4**i)*start) |
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zs = _sample(zs, labels, priors, sample_levels, hps) |
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for z, z_in in zip(zs, zs_in): |
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total_length = z.shape[1] |
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prime_length = z.shape[1] // (4 * hps.n_segment) |
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assert (z[:,:prime_length] == z_in[:,:prime_length]).all() |
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z_rest = z[:,prime_length-1:] - z[:,prime_length-1:prime_length] |
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assert ((z_rest - t.arange(0, total_length - prime_length + 1, dtype=t.long, device='cuda').view(1, total_length - prime_length + 1)) == 0).all() |
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print("dummy primed sample passed") |
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def check_sample(): |
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n_ctx = 8192 |
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n_samples = 4 |
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levels = 3 |
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priors = [DummyPrior(n_ctx, level, levels) for level in range(levels)] |
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max_total_length, offset, sample_length = 4134368, 0, n_ctx*8*4*4 |
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y = t.tensor([max_total_length, offset, sample_length, 10, 1, -1, -1, -1, -1], dtype=t.long, device='cuda').view(1, 9).repeat(n_samples, 1) |
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labels = [dict(y=y, info=[[]*n_samples]) for level in range(levels)] |
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hps = Hyperparams({ |
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'levels': 3, |
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'sample_length': sample_length, |
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'n_segment': 2, |
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'n_ctx': n_ctx, |
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'n_tokens': 0, |
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'hop_lengths': [n_ctx//2, n_ctx//2, n_ctx//8], |
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'n_samples': n_samples, |
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'use_tokens': False |
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}) |
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test_ancestral_sample(labels, priors, hps) |
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test_primed_sample(labels, priors, hps) |
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check_sample() |
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